Voting is one of the methods for estimation of an unknown parameter from known measurements. Depending upon the algorithm, measurements that are incorrect and/or imprecise can be accommodated and hopefully do not impair the result. In principle, each measurement votes for some (discrete) values of the unknown parameter. Each vote can have a different weight. After counting all the votes, the parameter value with most votes is chosen as the most probable solution. If the measurements are imprecise, and the error tolerance interval is known, the votes are spread over several values, covering the intervals. With enough imprecise measurements the most probable value might still be found in the intersection of the tolerance intervals. The solution is found at the value most voted for. It does not have to have a majority of the votes, but a maximum. So if the incorrect measurements are uniformly distributed, they do not affect the solution even if they are many, as the maximum is preserved. But if the incorrect measurements are concentrated, they may overweight the correct value and ruin the result.
There are several advantages to the voting approach. First, the most probable value is always found with a single iteration over all measurements. There is no random process involved, as in RANSAC method. Second, it is easy to accommodate for all the different distributions and confidence intervals of the votes. It is also easy to formulate it statistically. If the probability can be expressed of a measurement given a value of the sought parameter, the probability can be exactly the number by which is the corresponding accumulator bin is incremented. There are also disadvantages. The main is that the voting approach is suitable only for low-dimensional problems. The number of bins in the accumulator is the product of the number of possible values in each dimension, which in turn is given by the range of the values and their discretisation. If each dimension is quantised to hundreds or thousands of values, the practical usability stops at two dimensional problems. Then the memory required to store the accumulator is large as well as with the computational cost of traversal operations (initialization and maximum search). A second disadvantage is that adding a vote can be computationally expensive if it spans a large number of accumulator bins or it is costly to evaluate which bins it covers. Usually 2D votes have the fowl of straight lines or line segments, which are easy enough to discretise.